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Advance on Net Surface Radiation Inversion Using Remote Sensing Technology

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 16957

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Prof. Dr. Bo-Hui Tang

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1. Faculty of Land Resource Engineering, Kunming University of Science and Technology (KUST), Kunming 650093, China
2. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: retrieval and validation of land surface temperature/emissivity; retrieval and validation of net surface radiation; radiative transfer modeling; quantitative estimation of land surface variables from middle infrared data; hyperspectral thermal infrared data analysis
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Dr. Sibo Duan

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Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Academy of Agricultural Sciences, Beijing 100081, China
Interests: thermal infrared remote sensing; land surface temperature; land surface emissivity; radiative transfer modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Liang Huang

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Guest Editor

Faculty of Land Resource Engineering, Kunming University of Science and Technology (KUST), Kunming 650093, China
Interests: change detection; image segmentation; identification of crop type and plant type; target detection

Special Issue Information

Dear Colleagues,

Net surface radiation (NSR), consisting of net surface shortwave radiation (NSSR) and net surface longwave radiation (NSLR), is the driving force for the process of the surface energy balance and land–atmosphere interactions, and is vitally important for various applications including climate studies, agricultural meteorology, and regional and global land surface models studies.

Up to now, many efforts have been made to estimate NSR using remote sensing data, and many parameterization schemes have been successfully developed to estimate NSR, NSSR, and NSLR from satellite-based data. However, it should be pointed out that most of the methods are just for the estimations of NSR, NSSR, and NSLR for clear-sky days. The estimations of above variables over cloudy skies are still problematic.

Note that the daily average net surface radiation (DANSR) has more meaning than instantaneous NSR for atmospheric and hydrologic modeling and the estimate of evapotranspiration from remotely sensed data. Currently, estimation of DANSR is usually using one single instantaneous NSR with a sinusoidal model deduced from the diurnal cycle of surface temperature for clear days. This clear-based sinusoidal approximation model may not accurately capture the diurnal variation of NSR for days when cloud cover was present during a portion or entire day.

This Special Issue is being planned to focus on the latest advance on retrieval and validation of NSR, NSSR, and NSLR from space measurements and grow interest in estimations and analyses of those parameters.

Dr. Bo-Hui Tang
Dr. Si-Bo Duan
Dr. Liang Huang
Guest Editors

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Keywords

Net surface radiation (NSR)
Net surface shortwave radiation
Net surface longwave radiation
Downwelling surface shortwave radiation
Downwelling surface longwave radiation
Upwelling surface longwave radiation
Surface albedo
Land surface temperature/emissivity
NSR validation
Daily net surface radiation

Published Papers (8 papers)

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Research

20 pages, 6243 KiB

Open AccessArticle

Estimating All-Weather Surface Longwave Radiation from Satellite Passive Microwave Data

by Zhonghu Jiao

Remote Sens. 2022, 14(23), 5960; https://doi.org/10.3390/rs14235960 - 25 Nov 2022

Cited by 1 | Viewed by 1555

Abstract

Surface longwave radiation (SLR) is an essential geophysical parameter of Earth’s energy balance, and its estimation based on thermal infrared (TIR) remote sensing data has been extensively studied. However, it is difficult to estimate cloudy SLR from TIR measurements. Satellite passive microwave (PMW) radiometers measure microwave radiation under the clouds and therefore can estimate SLR in all weather conditions. We constructed SLR retrieval models using brightness temperature (BT) data from an Advanced Microwave Scanning Radiometer 2 (AMSR2) based on a neural network (NN) algorithm. SLR from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) product was used as the reference. NN-based models were able to reproduce well the spatial variability of SLR from ERA5 at the global scale. Validations indicate a reasonably good performance was found for land sites, with a bias of 1.32 W/m², root mean squared error (RMSE) of 35.37 W/m², and coefficient of determination (R²) of 0.89 for AMSR2 surface upward longwave radiation (SULR) data, and a bias of −2.26 W/m², RMSE of 32.94 W/m², and R² of 0.82 for AMSR2 surface downward longwave radiation (SDLR) data. AMSR2 SULR and SDLR retrieval accuracies were higher for oceanic sites, with biases of −2.98 and −4.04 W/m², RMSEs of 6.50 and 13.42 W/m², and R² values of 0.83 and 0.66, respectively. This study provides a solid foundation for the development of a PMW SLR retrieval model applicable at the global scale to generate long-term continuous SLR products using multi-year satellite PMW data and for future research with a higher spatiotemporal resolution. Full article

(This article belongs to the Special Issue Advance on Net Surface Radiation Inversion Using Remote Sensing Technology)

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21 pages, 16113 KiB

Open AccessArticle

STF-EGFA: A Remote Sensing Spatiotemporal Fusion Network with Edge-Guided Feature Attention

by Feifei Cheng, Zhitao Fu, Bohui Tang, Liang Huang, Kun Huang and Xinran Ji

Remote Sens. 2022, 14(13), 3057; https://doi.org/10.3390/rs14133057 - 25 Jun 2022

Cited by 5 | Viewed by 1981

Abstract

Spatiotemporal fusion in remote sensing plays an important role in Earth science applications by using information complementarity between different remote sensing data to improve image performance. However, several problems still exist, such as edge contour blurring and uneven pixels between the predicted image and the real ground image, in the extraction of salient features by convolutional neural networks (CNNs). We propose a spatiotemporal fusion method with edge-guided feature attention based on remote sensing, called STF-EGFA. First, an edge extraction module is used to maintain edge details, which effectively solves the boundary blurring problem. Second, a feature fusion attention module is used to make adaptive adjustments to the extracted features. Among them, the spatial attention mechanism is used to solve the problem of weight variation in different channels of the network. Additionally, the problem of uneven pixel distribution is addressed with a pixel attention (PA) mechanism to highlight the salient features. We transmit the different features extracted by the edge module and the encoder to the feature attention (FA) module at the same time after the union. Furthermore, the weights of edges, pixels, channels and other features are adaptively learned. Finally, three remote sensing spatiotemporal fusion datasets, Ar Horqin Banner (AHB), Daxing and Tianjin, are used to verify the method. Experiments proved that the proposed method outperformed three typical comparison methods in terms of the overall visual effect and five objective evaluation indexes: spectral angle mapper (SAM), peak signal-to-noise ratio (PSNR), spatial correlation coefficient (SCC), structural similarity (SSIM) and root mean square error (RMSE). Thus, the proposed spatiotemporal fusion algorithm is feasible for remote sensing analysis. Full article

(This article belongs to the Special Issue Advance on Net Surface Radiation Inversion Using Remote Sensing Technology)

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16 pages, 3093 KiB

Open AccessArticle

Estimation of Downwelling Surface Longwave Radiation with the Combination of Parameterization and Artificial Neural Network from Remotely Sensed Data for Cloudy Sky Conditions

by Yun Jiang, Bo-Hui Tang and Yanhong Zhao

Remote Sens. 2022, 14(11), 2716; https://doi.org/10.3390/rs14112716 - 06 Jun 2022

Cited by 5 | Viewed by 2061

Abstract

This work proposes a new method for estimating downwelling surface longwave radiation (DSLR) under cloudy-sky conditions based on a parameterization method and a genetic algorithm–artificial neural network (GA-ANN) algorithm. The new method establishes a GA-ANN model based on simulated data, and then combines MODIS satellite data and ERA5 reanalysis data to estimate the DSLR. According to the validation results of the field sites, the bias and RMSE are –9.18 and 34.88 W/m², respectively. Compared with the existing research, the new method can achieve reasonable accuracy. Parameter analysis using independently simulated data shows that the near-surface air temperature (

T_{a}

) and cloud base height (CBH) have an important influence on DSLR estimation under cloudy-sky conditions. With an increase in CBH, DSLR gradually decreases; however, with an increase in

T_{a}

, DSLR shows a trend of gradual increase. When estimating DSLR under cloudy-sky conditions, under the influence of clouds, except for cirrus, the change in DSLRs with CBH and

T_{a}

is greater than 20 W/m². Full article

(This article belongs to the Special Issue Advance on Net Surface Radiation Inversion Using Remote Sensing Technology)

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26 pages, 2943 KiB

Open AccessArticle

Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation

by Francis M. Lopes, Emanuel Dutra and Isabel F. Trigo

Remote Sens. 2022, 14(7), 1704; https://doi.org/10.3390/rs14071704 - 01 Apr 2022

Cited by 8 | Viewed by 2055

Abstract

The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m⁻² are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model. Full article

(This article belongs to the Special Issue Advance on Net Surface Radiation Inversion Using Remote Sensing Technology)

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27 pages, 10668 KiB

Open AccessArticle

Evaluation of Surface Upward Longwave Radiation in the CMIP6 Models with Ground and Satellite Observations

by Jiawen Xu, Xiaotong Zhang, Chunjie Feng, Shuyue Yang, Shikang Guan, Kun Jia, Yunjun Yao, Xianhong Xie, Bo Jiang, Jie Cheng and Xiang Zhao

Remote Sens. 2021, 13(21), 4464; https://doi.org/10.3390/rs13214464 - 06 Nov 2021

Cited by 4 | Viewed by 2100

Abstract

Surface upward longwave radiation (SULR) is an indicator of thermal conditions over the Earth’s surface. In this study, we validated the simulated SULR from 51 Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) through a comparison with ground measurements and satellite-retrieved SULR from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF). Moreover, we improved the SULR estimations by a fusion of multiple CMIP6 GCMs using multimodel ensemble (MME) methods. Large variations were found in the monthly mean SULR among the 51 CMIP6 GCMs; the bias and root mean squared error (RMSE) of the individual CMIP6 GCMs at 133 sites ranged from −3 to 24 W m⁻² and 22 to 38 W m⁻², respectively, which were higher than those found between the CERES EBAF and GCMs. The CMIP6 GCMs did not improve the overestimation of SULR compared to the CMIP5 GCMs. The Bayesian model averaging (BMA) method showed better performance in simulating SULR than the individual GCMs and simple model averaging (SMA) method, with a bias of 0 W m⁻² and an RMSE of 19.29 W m⁻² for the 133 sites. In terms of the global annual mean SULR, our best estimation for the CMIP6 GCMs using the BMA method was 392 W m⁻² during 2000–2014. We found that the SULR varied between 386 and 393 W m⁻² from 1850 to 2014, exhibiting an increasing tendency of 0.2 W m⁻² per decade (p < 0.05). Full article

(This article belongs to the Special Issue Advance on Net Surface Radiation Inversion Using Remote Sensing Technology)

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20 pages, 5793 KiB

Open AccessArticle

Field-Based High-Quality Emissivity Spectra Measurement Using a Fourier Transform Thermal Infrared Hyperspectral Imager

by Lyuzhou Gao, Liqin Cao, Yanfei Zhong and Zhaoyang Jia

Remote Sens. 2021, 13(21), 4453; https://doi.org/10.3390/rs13214453 - 05 Nov 2021

Cited by 5 | Viewed by 1906

Abstract

Emissivity information derived from thermal infrared (TIR) hyperspectral imagery has the advantages of both high spatial and spectral resolutions, which facilitate the detection and identification of the subtle spectral features of ground targets. Despite the emergence of several different TIR hyperspectral imagers, there are still no universal spectral emissivity measurement standards for TIR hyperspectral imagers in the field. In this paper, we address the problems encountered when measuring emissivity spectra in the field and propose a practical data acquisition and processing framework for a Fourier transform (FT) TIR hyperspectral imager—the Hyper-Cam LW—to obtain high-quality emissivity spectra in the field. This framework consists of three main parts. (1) The performance of the Hyper-Cam LW sensor was evaluated in terms of the radiometric calibration and measurement noise, and a data acquisition procedure was carried out to obtain the useful TIR hyperspectral imagery in the field. (2) The data quality of the original TIR hyperspectral imagery was improved through preprocessing operations, including band selection, denoising, and background radiance correction. A spatial denoising method was also introduced to preserve the atmospheric radiance features in the spectra. (3) Three representative temperature-emissivity separation (TES) algorithms were evaluated and compared based on the Hyper-Cam LW TIR hyperspectral imagery, and the optimal TES algorithm was adopted to determine the final spectral emissivity. These algorithms are the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm, the improved Advanced Spaceborne Thermal Emission and Reflection Radiometer temperature and emissivity separation (ASTER-TES) algorithm, and the Fast Line-of-sight Atmospheric Analysis of Hypercubes-IR (FLAASH-IR) algorithm. The emissivity results from these different methods were compared to the reference spectra measured by a Model 102F spectrometer. The experimental results indicated that the retrieved emissivity spectra from the ISSTES algorithm were more accurate than the spectra retrieved by the other methods on the same Hyper-Cam LW field data and had close consistency with the reference spectra obtained from the Model 102F spectrometer. The root-mean-square error (RMSE) between the retrieved emissivity and the standard spectra was 0.0086, and the spectral angle error was 0.0093. Full article

(This article belongs to the Special Issue Advance on Net Surface Radiation Inversion Using Remote Sensing Technology)

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31 pages, 10687 KiB

Open AccessArticle

Hyperspectral Prediction of Soil Total Salt Content by Different Disturbance Degree under a Fractional-Order Differential Model with Differing Spectral Transformations

by Anhong Tian, Junsan Zhao, Bohui Tang, Daming Zhu, Chengbiao Fu and Heigang Xiong

Remote Sens. 2021, 13(21), 4283; https://doi.org/10.3390/rs13214283 - 25 Oct 2021

Cited by 9 | Viewed by 1802

Abstract

Soil salinization is an ecological challenge across the world. Particularly in arid and semi-arid regions where evaporation is rapid and rainfall is scarce, both primary soil salinization and secondary salinization due to human activity pose serious concerns. Soil is subject to various human disturbances in Xinjiang in this area. Samples with a depth of 0–10 cm from 90 soils were taken from three areas: a slightly disturbed area (Area A), a moderately disturbed area (Area B), and a severely disturbed area (Area C). In this study, we first calculated the hyperspectral reflectance of five spectra (R,

\sqrt{R}

, 1/R, lgR, 1/lgR, or original, root mean square, reciprocal, logarithm, and reciprocal logarithm, respectively) using different fractional-order differential (FOD) models, then extracted the bands that passed the 0.01 significance level between spectra and total salt content, and finally proposed a partial least squares regression (PLSR) model based on the FOD of the significance level band (SLB). This proposed model (FOD-SLB-PLSR) is compared with the other three PLSR models to predict with precision the total salt content. The other three models are All-PLSR, FOD-All-PLSR, and IOD-SLB-PLSR, which respectively represent PLSR models based on all bands, all fractional-order differential bands, and significance level bands of the integral differential. The simulations show that: (1) The optimal model for predicting total salt content in Area A was the FOD-SLB-PLSR based on a 1.6 order 1/lgR, which provided good predictability of total salt content with a RPD (ratio of the performance to deviation) between 1.8 and 2.0. The optimal model for predicting total salt content in Area B was a FOD-SLB-PLSR based on a 1.7 order 1/R, which showed good predictability for total salt content with RPDs between 2.0 and 2.5. The optimal model for predicting total salt content in Area C was a FOD-SLB-PLSR based on a 1.8 order lgR, which also showed good predictability for total salt content with RPDs between 2.0 and 2.5. (2) Soils subject to various disturbance levels had optimal FOD-SLB-PLSR models located in the higher fractional order between 1.6 and 1.8. This indicates that higher-order FODs have a stronger ability to extract feature data from complex information. (3) The optimal FOD-SLB-PLSR model for each area was superior to the corresponding All-PSLR, FOD-All-PLSR, and IOD-SLB-PLSR models in predicting total salt content. The RPD value for the optimal FOD-SLB-PLSR model in each area compared to the best integral differential model showed an improvement of 9%, 45%, and 22% for Areas A, B, and C, respectively. It further showed that the fractional-order differential model provides superior prediction over the integral differential. (4) The RPD values that provided an optimal FOD-SLB-PLSR model for each area were: Area A (1.9061) < Area B (2.0761) < Area C (2.2892). This indicates that the prediction effect of data processed by fractional-order differential increases with human disturbance increases and results in a higher-precision model. Full article

(This article belongs to the Special Issue Advance on Net Surface Radiation Inversion Using Remote Sensing Technology)

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23 pages, 20525 KiB

Open AccessArticle

Study on the Pretreatment of Soil Hyperspectral and Na⁺ Ion Data under Different Degrees of Human Activity Stress by Fractional-Order Derivatives

by Anhong Tian, Junsan Zhao, Bohui Tang, Daming Zhu, Chengbiao Fu and Heigang Xiong

Remote Sens. 2021, 13(19), 3974; https://doi.org/10.3390/rs13193974 - 04 Oct 2021

Cited by 7 | Viewed by 1578

Abstract

Soluble salts in saline soil often exist in the form of salt base ions, and excessive water-soluble base ions can harm plant growth. As one of the water-soluble base ions, Na+ ion, is the main indicator of the degree of soil salinization. The pretreatment of visible, near infrared and short-wave infrared (VNIR-SWIR) spectroscopy data is the key to establishing a high-precision inversion model, and a proper pretreatment method can fully extract the effective information hidden in the hyperspectral data. Meanwhile, different degrees of human activity stress will have an impact on the ecological environment of oases. However, there are few comparative analyses of the data pretreatment effects for soil water-soluble base ions on the environment under different human interference conditions. Therefore, in this study, the difference in the degree of soil disturbance caused by human activities was used as the basis for dividing the experimental area into lightly disturbed area (Area A), moderately disturbed area (Area B) and severely disturbed zone (Area C). The Grünwald-Letnikov fractional-order derivative (FOD) was used to preprocess the VNIR-SWIR spectroscopic data measured by a FieldSpec®3Hi-Res spectrometer, which could fully extract the useful information hidden in the FOD of the VNIR-SWIR spectroscopy results and avoid the loss of information caused by the traditional integer-order derivative (1.0-order, 2.0-order) pretreatment. The spectrum pretreatment was composed of five transform spectra (R,

\sqrt{R}

, 1/R, lgR, 1/lgR) and 21 FOD methods (step size is 0.1, derivative range is from 0.0- to 2.0-order). In addition, this manuscript compares and analyzes the pretreatment advantages between fractional-order and integer-order. The main results were as follows: (1) Grünwald-Letnikov FOD can reveal the nonlinear characteristics and variation laws of the field hyperspectral of saline soil, namely, due to the continuous performance of the order selection, the FOD accurately depicts the details of spectral changes during the derivation process, and improves the resolution between the peaks of the hyperspectral spectrum. (2) There is a big difference in the shape of the correlation coefficient curve between the original hyperspectral and Na+ at different FOD. The correlation coefficient curve has a clear outline in rang of the 0.0- to 0.6-order, and the change trend is gentle, which presents a certain gradual form. With the continuous increase of the order of the FOD, the change range of the correlation coefficient curve is gradually increased, and the fluctuation is greater between the 1.0-order and the 2.0-order. (3) Regardless of the transformation spectrum and different interference regions, the improvement effect of the FOD on the correlation between hyperspectral and Na+ is significantly better than that of the integer-order derivative. Comparative analysis shows that he percentage of increase of the former is more than 3%, and the highest is more than 17%. Full article

(This article belongs to the Special Issue Advance on Net Surface Radiation Inversion Using Remote Sensing Technology)

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